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102 results for “Assessment indicators”
Value chains under the framework of life cycle assessment indicators
<p>Tables included in the article "Monitoring the bioeconomy: value chains under the framework of life cycle assessment indicators"</p>
Results from retrospective Baltic Sea biodiversity indicator status assessment using BEAT 3.0 tool
<p>Here we present all our results from retrospective Baltic Sea biodiversity indicator data analysis using BEAT 3.0. The BEAT tool (Nygård et al. 2018) is an R coded software (Murray & Nygård 2018, available online: <a href="https://zenodo.org/record/1288315#.XRxNp2cXYg4">https://zenodo.org/record/1288315#.XRxNp2cXYg4</a>) developed for analyzing marine biodivesity status. It follows the strucuture of EU's Marine Strategy Framework Directive. For more detailed metadata about the tool, see Nygård et al. 2018.</p> <p>We used data from various biodiversity indicators in two areas of the Baltic Sea: Bothnian Sea and Gulf of Finland. BEAT integrates indicators to ecosystem components and aggregates them spatially (more details can be found in Nygård et al. 2018). We produced retrospective time series of integrated and aggregated indicators. The yearly assessments are done using the moving average of the indicator status of the past 5 years in order to gain a more robust assessment result. The assessment follows the protocol of biodiversity assessment in the HELCOM Holistic assessment <a href="http://www.helcom.fi/baltic-sea-trends/holistic-assessments">http://www.helcom.fi/baltic-sea-trends/holistic-assessments</a></p> <p>In the results table, all different spatial levels as well as ecosystem components are shown. All indicator results have a value between 0 and 1. If the indicator has a value over 0.6, it is considered to be in a good environmental status. Below are short description of the different columns:</p> <ul> <li>SAUID: ID of the spatial assessment unit (SAU) used. The largest SAU is Baltic Sea with an ID 1. It is divided to smaller SAUs and all individual SAUs have their own ID.</li> <li>SAUlevel: The highest possible level of SAU is the Baltic Sea and it is the level 1. The sea basins (for example Bothnian Sea) are the level 2 and so on.</li> <li>ECID: Ecosystem component ID. All possible indicators have their own ID. See the list of ecosystem components in the input files of the tool (Murray & Nygård 2018).</li> <li>EClevel: Ecosystem component level. The level 1 is biodiversity, in the level 2 it is divided to pelagic habitat, birds, fish, benthic habitat and mammals and so on.</li> <li>EcosystemComponent: this column tells the ecosystem component. It can be higher level e.g. biodversity or an individual indicator for certain taxa.</li> <li>EQR: ecological quality ratio. The status of the certain ecosystem component in a certain SAU. The value varies between 0 and 1. If it is over 0.6, the ecosystem component is considered to be in a good status.</li> <li>Columns H-T: These refer to certain descriptors of Marine Strategy Framework Directive. If the ecosystem component is considered to have a link to a certain descriptor, an EQR value is given.</li> <li>year: year of the assessment. Note: all the yearly values are a moving average of past 5 years.</li> </ul> <p> </p>
Sampling sites where ecological indices were used to assess the impact of different environmental stressors in aquatic environments in Argentina
Dataset is a compilation of all sampling sites of articles where ecological indices were used to assess the impact of different environmental stressors in aquatic environments from Argentina. Points of this dataset were extracted from 78 papers published between 1996 and 2018. We selected articles that use ecological indices to analyze some local environmental problematics or stressors. Using the type of index from each article we performed the kml file, which contained the categorized sampling sites by different symbols according to the ecological index: physico-chemical, biological, geomorphological and multimetric. We carried out a map (shapefile) with all the sampling sites referenced to the ecoregions of Argentina proposed by Burkart (1999).
Mapping Building BioData.pt Indicators against the performance and impact assessment frameworks for research infrastructures of OECD, ESFRI and RI-PATHS project
<p>"Buiding BioData.pt" indicators observed in international frameworks for performance and impact assessment of research infrastructures, namely, OECD, ESFRI and RI-PATHS.</p>
Relevance of "Building BioData.pt" indicators identified in ESFRI (E), OECD (O) and RI-PATHS (R) impact assessment frameworks
<p>The relevance of the indicators maintained by the "Building BioData.pt" project was assessed against the objectives of different organizations/initiatives: 1) Strategic objectives of BioData.pt; 2) Objectives of the Portuguese Roadmap for Research Infrastructures; 3) Objectives of ELIXIR; 4) Objectives of EOSC; 5) Sustainable Development Goals of the United Nations.</p>
Riverine Flood Insurance assessment indicators under climate and socio-economic change
<p>Expected annual river flood damages, flood insurance premiums, and insurance penetration rates, for EU-regions (NUTS2) and under future climatic and socio-economic conditions (RCP-SSP combinations).</p>
Sustainable Aging in Aix-Marseille Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making
<p>This upload contains a geopackage file, R code, Readme file and an HTML-based platform with interactive maps created using the geopackage and R code within the ForVie project (Forme et vieillissement au sein de la Métropole d'Aix-Marseille-Provence). This upload presents various data, indicators and interactive maps to help planners and policymakers to quickly identify patterns and trends related to aging within Aix-Marseille metropolis, France.</p> <p>Authors<br> Perez, J & Boyer, T. Université Côte d'Azur, UMR 7300 ESPACE-CNRS, Nice, France.</p> <p>Files<br> * `AGING_AMP_INDICATORS.gpkg`: The geopackage file contains spatial data used in the R code to create the maps.<br> * `code_R_platform_v1.0_ForVie.R`: The R code used to create the interactive maps.<br> * `ForVie_platform_v1.0.html`: The platform for visualizing the interactive maps.</p> <p>Instructions<br> To use this upload, follow these steps:<br> 1. Download the geopackage file, R code, and index.html file.<br> 2. Open the R code in RStudio or another R environment.<br> 3. Install any necessary packages as listed in the first section of the R code.<br> 4. Run the code in R to create the HTML-based platform with interactive maps<br> Alternatively, it is possible to directly open the ForVie_platform_v1.0.html file in a web browser to view the maps.</p> <p>Acknowledgment<br> This research was funded by a grant from Région Sud, Provence-Alpes-Côte d'Azur, France (ForVie).</p> <p>References<br> Perez et al., (2023) "Sustainable Aging in Aix-Marseille Metropolis: Assessment Indicators and Interactive Visualizations for Policy Making." Submitted (reference to be updated)</p> <p>Contact<br> If you have any questions about this upload, please contact Perez Joan at joan.perez@univ-cotedazur.fr</p>
Tool for the selection of indicators for agriculture sustainability assessment
<p>This is a preliminary version of the Excel-based tool that is being developed within the Working Group of the Crop diversification cluster on Multi-criteria Assessment and Sustainability Indicators (MCA & SI cluster WG). The tool has the aim to assist different end-users in the selection of sustainability indicators tailored to assess the impacts and effects of crop diversification. Currently this preliminary version contains only the indicators developed by the DiverIMPACTS and DiverFARMING projects (2 out 5 projects of Crop diversification cluster).</p> <p>The tool is composed by three sheets: 1) the “Selection” sheet for querying the database and obtaining the results; 2) the “Database” sheet where all indicators of the cluster projects are reported; 3) the “SAFA_HierachicalClassification” sheet with the description of the SAFA framework, and the items identified for the indicators of the cluster projects reported in the database.</p>
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021). in Floristic, Vegetation And Climate Assessment Of The Early/Middle Miocene Parschlug Flora Indicates A Distinctly Seasonal Climate
Text-fig. 1. Modern vegetation proxies as delivered by the Drudge 1 and 2 tools for Parschlug. Left column results from KovarEder et al. (2021) based on the floristic spectrum published by Kovar-Eder et al. (2004). The other three columns result from three variants using the enlarged floristic spectrum herein. Differences between variants 1–3 from this study are caused by differences in assignment of some taxa and morphotypes (see Appendix 1). European vegetation formations: Formation C – Subarctic, boreal and nemoral-montane open woodlands as well as subalpine and oro-Mediterranean vegetation; Formation D – Mesophytic and hygromesophytic coniferous and mixed broad-leaved-coniferous forests; Formation F – Mesophytic broadleaved deciduous and mixed broadleaved/conifer forests; Formation G – Thermophilous mixed deciduous broadleaved forests; Formation J – Mediterranean sclerophyllous forests and scrub; Formation K – Xerophytic coniferous forests, coniferous woodland and scrub. East Asian vegetation types: MCF China, Japan – Montane Coniferous Forests China, Honshu, Yakushima; BLDF N and NE Provinces, China – Broad-leaved Deciduous Forests of the Northern and Northeastern Provinces (China); BLDF Upper Yangtze, Honshu – Broad-leaved Deciduous Forest, Upper Yangtze Provinces, Mt. Emei, and Honshu; MMF China – Mixed Mesophytic Forest, Lower Yangtze Provinces; BLEF China, Japan – Broad-leaved Evergreen Forests, China, Japan; Meili Snow Mt. high altitude SCL and BLF, China – Meili Snow Mt., Sclerophyllous and broad-leaved forest zone (2,580-3,650 m alt.). (Designations of European vegetation formations follow Bohn et al. (2004) and Asian ones follow Kovar-Eder et al. (2021).
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7). in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 4. Graphical visualization of Phytogeographic Reference Regions Assessment (PRRA) of nearest living relative genera of fossil-taxa from late Early Miocene Wiesa assemblage in eastern Germany. Analysis yields only NLRs which have modern distribution area (partly) in E and SE Asia. For relationships of fossil-taxa to nearest living relatives or ecological equivalents, see Tab. 6; taxa used for analysis marked with asterisks. Three geographic resolutions conducted: a – grid with 1.5° latitude/longitude resolution, b – grid with 2°, c – grid with 3°; similarity column indicates cooccurrences of genera of nearest living relatives in single grid box. Maximum value in our analysis: grid box marked with arrow in map a, located in western Yunnan Province, P. R. China and southern Kachin Province, NE Myanmar (east of Myitkyina city), area with 97.371 7–98.874 2° longitude and 24.586 7–25.837 5° latitude, yields 23 co-occurring species of 13 genera (Tab. 7).
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 3. Litho- and biostratigraphic position of fossil floras treated herein, based on lithostratigraphic standard section of upper Oligocene and Miocene in central and eastern Germany (Standke et al. 2010, Escher et al. 2020); only exception from standard section: ** – Thierbach Member restricted to central Germany, replaces Branitz Member in eastern Germany; correlated to global scale of International Chronostratigraphic Chart 2022/02 (Cohen et al. 2013); maximum age ranges of sites/floras indicated by black bars; floristic complexes according to definitions by Mai and Walther 1991 for upper Oligocene, Mai 2000b, 2001b for Miocene; age range of MCO from Steinthorsdottir et al. 2021.
Multidimensional impact assessment of a large collection of books using PlumX: methodology, technical limitations and indicators analysis [Complementary material to manuscript]
<p>The main purpose of this macro-study is to shed light on the broad impact of books. For this purpose, the impact a very large collection of books (more than 200,000) has been analysed by using PlumX, an analytical tool providing a great number of different metrics provided by various tools. Furthermore, the study focuses on the evolution of the most significant measures and indicators over time. The results show usage counts in comparison to the other metrics are quantitatively predominant. Catalogue holdings and reviews represent a book’s most characteristic measures deriving from its increased level of impact in relation to prior results. Our results also corroborate the long half-life of books within the scope of all metrics, excluding views and social media. Despite of some disadvantages, PlumX has proved to be a very helpful and promising tool for assessing the broad impact of books, especially because of how easy it is to enter the ISBN directly as well as its algorithm to aggregate all the data generated by the different ISBN variations.</p>
Fig. 6 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 6. Sábalo fishery status according to spawning potential ratio (SPR) and fishing mortality (F) variability across time. TRP: Target reference point; LRP: Limit reference point. PS76: Puerto Sánchez 1976; PS02: Puerto Sánchez 2002; V89: Victoria 1989; V94: Victoria 94; V05: Victoria 2005; H05: Helvecia 2005.
Fig. 5. a in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 5. a) Analysis of yield per recruitment for different size at first capture. b)Spawning potential ratio (SPR) over time based on the estimated exploitation rate (μ) and the capture length. PS76: Puerto Sánchez 1976; PS02: Puerto Sánchez 2002; V89: Victoria 1989; V94: Victoria 94; V05: Victoria 2005; H05: Helvecia 2005.
Fig. 4 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 4. Variation in the mean length (wide bars), the maximum length (thin bars), and the mesh size (points) between 1976 and 2005.
Fig. 2 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 2. Capture records of sábalo in the Paraná basin. The data taken from 1920 to 1988 were compiled by the National Agency of Freshwater Fisheries. The data from 1994 to 2009 correspond to the exportation records of the Sanitary National Service (SENASA). No data from 1988 to 1993 were available.
Fig. 1 in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 1. Study area and the location of the main landing sites of the sábalo fisheries in the lower Paraná basin.
Fig. 3. a in Assessment of sábalo (Prochilodus lineatus) fisheries in the lower Paraná River basin (Argentina) based on hydrological, biological, and fishery indicators
Fig. 3. a) Variation of average flow from 1920 to 2009. b) Variation in the channel-connectivity index (CCI) from 1920 to 2009, expressed as a percent of the time that the water level surpasses the bankfull level (3 m). c) Variation of the floodplain connectivity index (FCI) from 1920 to 2009, expressed as the percentage of the time that the water level surpassed a height of 4.3 m flooding the alluvial valley. Vertical grey lines indicate different hydrological periods considered in this study.
Fig. 1 in Assessment of Odonate assemblages in the Agro Pontino (Latina, Central Italy) using two biotic indices (Insecta: Odonata)
Fig. 1 – Geographical location of the study area (Lazio, central Italy). On the right: geographical location within Italy
Supplement code and data for "A global multi-indicator assessment of the environmental impact of livestock products"
<p>This data and code supplements the publication "A global multi-indicator assessment of the environmental impact of livestock products" by Giorgio A. Bidoglio, Florian Schwarzmueller, Thomas Kastner available here: https://doi.org/10.1016/j.gloenvcha.2024.102853<br><br></p> <p>Version 1.1 add the code to the visualization tool available online at https://livestockimpactassessment.shinyapps.io/multi-indicator_impact_assessment_of_livestock_products/</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.